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Simon-Bertrand / Clusters-Features

The Clusters-Features package allows data science users to compute high-level linear algebra operations on any type of data set. It computes approximatively 40 internal evaluation scores such as Davies-Bouldin Index, C Index, Dunn and its Generalized Indexes and many more ! Other features are also available to evaluate the clustering quality.

python internal validation clustering evaluation unsupervised-learning clusters pbm indices clustering-analysis ball-hall generalized-dunn-indexes c-index banfeld-raftery davies-bouldin calinski-harabasz ray-turi xie-beni wemmert-gancarski point-biserial

Updated Mar 11, 2025
Python

neural-tangjie / NTJ-AIMed_2_Prognosis

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:octocat: This repository contains the notes, codes, assignments, quizzes and other additional materials about the course "AI for Medical Prognosis" from DeepLearning.AI Coursera.

numpy hazard pandas imputation linear-model missing-data survival kaplan-meier decision-tree time-to-event risk-model heart-disease overfitting mortality-rates censored-data prognosis c-index concordance-index

Updated Jan 1, 2023
Jupyter Notebook

Lizhecheng02 / Kaggle-CIBMTR

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In this competition, you’ll develop models to improve the prediction of transplant survival rates for patients undergoing allogeneic Hematopoietic Cell Transplantation (HCT) — an important step in ensuring that every patient has a fair chance at a successful outcome, regardless of their background.

prediction gbdt lora post-processing c-index llm tabm